# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_movies = pd.read_csv(path + 'ottmovies.csv')
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13+ | 8.8 | 87% | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 1 | 2 | The Matrix | 1999 | 16+ | 8.7 | 88% | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13+ | 8.4 | 85% | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 3 | 4 | Back to the Future | 1985 | 7+ | 8.5 | 96% | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16+ | 8.8 | 97% | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161.0 | movie | NaN | 1 | 0 | 1 | 0 | 0 |
# profile = ProfileReport(df_movies)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 8457
IMDb 328
Rotten Tomatoes 10437
Directors 357
Cast 648
Genres 234
Country 303
Language 437
Plotline 4958
Runtime 382
Seasons 16923
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 49.973409
IMDb 1.938191
Rotten Tomatoes 61.673462
Directors 2.109555
Cast 3.829108
Genres 1.382734
Country 1.790463
Language 2.582284
Plotline 29.297406
Runtime 2.257283
Kind 0.000000
Seasons 100.000000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
df_movies.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.0 |
| mean | 8462.000000 | 2003.211901 | 0.214915 | 0.062637 | 0.727235 | 0.033150 | 0.0 |
| std | 4885.393638 | 20.526532 | 0.410775 | 0.242315 | 0.445394 | 0.179034 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 25% | 4231.500000 | 2001.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 50% | 8462.000000 | 2012.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| 75% | 12692.500000 | 2016.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| max | 16923.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.0 |
df_movies.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.217816 | -0.644470 | -0.129926 | 0.469301 | 0.263530 | NaN |
| Year | -0.217816 | 1.000000 | 0.256151 | 0.101337 | -0.255578 | -0.047258 | NaN |
| Netflix | -0.644470 | 0.256151 | 1.000000 | -0.118032 | -0.745141 | -0.089649 | NaN |
| Hulu | -0.129926 | 0.101337 | -0.118032 | 1.000000 | -0.284654 | -0.039693 | NaN |
| Prime Video | 0.469301 | -0.255578 | -0.745141 | -0.284654 | 1.000000 | -0.289008 | NaN |
| Disney+ | 0.263530 | -0.047258 | -0.089649 | -0.039693 | -0.289008 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
# udf_movies
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
df_movies_countries = df_movies.copy()
df_movies_countries.drop(df_movies_countries.loc[df_movies_countries['Country'] == "NA"].index, inplace = True)
# df_movies_countries = df_movies_countries[df_movies_countries.Country != "NA"]
# df_movies_countries['Country'] = df_movies_countries['Country'].astype(str)
df_movies_count_countries = df_movies_countries.copy()
df_movies_country = df_movies_countries.copy()
# Create countries dict where key=name and value = number of countries
countries = {}
for i in df_movies_count_countries['Country'].dropna():
if i != "NA":
#print(i,len(i.split(',')))
countries[i] = len(i.split(','))
else:
countries[i] = 0
# Add this information to our dataframe as a new column
df_movies_count_countries['Number of Countries'] = df_movies_count_countries['Country'].map(countries).astype(int)
df_movies_mixed_countries = df_movies_count_countries.copy()
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_countries_movies = df_movies_count_countries.loc[df_movies_count_countries['Netflix'] == 1]
hulu_countries_movies = df_movies_count_countries.loc[df_movies_count_countries['Hulu'] == 1]
prime_video_countries_movies = df_movies_count_countries.loc[df_movies_count_countries['Prime Video'] == 1]
disney_countries_movies = df_movies_count_countries.loc[df_movies_count_countries['Disney+'] == 1]
plt.figure(figsize = (10, 10))
corr = df_movies_count_countries.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
df_countries_most_movies = df_movies_count_countries.sort_values(by = 'Number of Countries', ascending = False).reset_index()
df_countries_most_movies = df_countries_most_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_countries['Number of Countries'] == (df_movies_count_countries['Number of Countries'].max()))
# df_countries_most_movies = df_movies_count_countries[filter]
# mostest_rated_movies = df_movies_count_countries.loc[df_movies_count_countries['Number of Countries'].idxmax()]
print('\nMovies with Highest Ever Number of Countries are : \n')
df_countries_most_movies.head(5)
Movies with Highest Ever Number of Countries are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Countries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6906 | Somewhere Else Tomorrow | 2013 | 13 | 7.5 | NA | Daniel Rintz | Issa Breibish,Kristian Bruun,Megan Gay,Grant J... | Documentary,Adventure,Drama,News | Germany,Canada,Vietnam,United Kingdom,Turkey,T... | ... | Promising young student Chi-Hao is sent away t... | 107 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 27 |
| 1 | 6333 | Poverty, Inc. | 2014 | NR | 7.7 | NA | Michael Matheson Miller | Robert Sirico | Documentary,History,News | United States,United Kingdom,Thailand,Swazilan... | ... | The lady is Mrs. Hilyard, a wealthy poetess wh... | 94 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 20 |
| 2 | 4382 | Samsara | 2011 | 13 | 8.5 | 76 | Ron Fricke | Balinese Tari Legong Dancers,Ni Made Megahadi ... | Documentary,Music | United States,Indonesia,Singapore,Thailand,Ken... | ... | NA | 102 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 19 |
| 3 | 8004 | Lost Kites | 2016 | NR | 7 | NA | Samuel Rich,Gabriella Fritz | NA | Documentary | United States,Belgium,Brazil,Cambodia,China,In... | ... | All fourteen-year-old Robbie Hendrick ever wan... | 50 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 18 |
| 4 | 16012 | Mulan II | 2004 | 0 | 5.7 | 0 | Darrell Rooney,Lynne Southerland | Ming-Na Wen,BD Wong,Mark Moseley,Lucy Liu,Harv... | Animation,Action,Comedy,Family,Musical | United States,South Korea,Singapore,Russia,Mal... | ... | Tia and her brother Tony have supernatural pow... | 79 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 15 |
5 rows × 21 columns
fig = px.bar(y = df_countries_most_movies['Title'][:15],
x = df_countries_most_movies['Number of Countries'][:15],
color = df_countries_most_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Highest Number of Countries : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_countries_least_movies = df_movies_count_countries.sort_values(by = 'Number of Countries', ascending = True).reset_index()
df_countries_least_movies = df_countries_least_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_countries['Number of Countries'] == (df_movies_count_countries['Number of Countries'].min()))
# df_countries_least_movies = df_movies_count_countries[filter]
print('\nMovies with Lowest Ever Number of Countries are : \n')
df_countries_least_movies.head(5)
Movies with Lowest Ever Number of Countries are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Countries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8436 | Citizen Architect: Samuel Mockbee and the Spir... | 2010 | NR | 7.6 | NA | Sam Wainwright Douglas | NA | Documentary,Biography,History | United States | ... | Apollo 13 was supposed to be the third human s... | 57 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 1 | 10816 | 72 Hours: Martyr Who Never Died | 2019 | NR | 5.2 | NA | Avinash Dhyani | Avinash Dhyani,Yeshi Dema,Alka Amin,Virendra S... | Action,Biography,Drama,War | India | ... | NA | 130 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 2 | 10817 | Looking for Lenny | 2011 | 13 | 6.2 | NA | Elan Gale | Lenny Bruce,Richard Lewis,Robin Williams,Rosea... | Documentary | United States | ... | Twenty years have passed since Dr. Vincent Edw... | 65 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 3 | 10818 | Forest Fairies | 2015 | 0 | 6.6 | NA | Justin G. Dyck | Emily Agard,Lora Burke,Brian Scott Carleton,Wi... | Adventure,Family,Fantasy | Canada | ... | Bank comptroller John Hewitt is a much-respect... | 90 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 4 | 10819 | The Unwilling | 2017 | 18 | 4.2 | 95 | Jonathan Heap | David Lipper,Dina Meyer,Bree Williamson,Robert... | Horror,Thriller | United States | ... | NA | 84 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
5 rows × 21 columns
fig = px.bar(y = df_countries_least_movies['Title'][:15],
x = df_countries_least_movies['Number of Countries'][:15],
color = df_countries_least_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Lowest Number of Countries : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_movies_count_countries['Number of Countries'].unique().shape[0]}' unique Number of Countries s were Given, They were Like this,\n
{df_movies_count_countries.sort_values(by = 'Number of Countries', ascending = False)['Number of Countries'].unique()}\n
The Highest Number of Countries Ever Any Movie Got is '{df_countries_most_movies['Title'][0]}' : '{df_countries_most_movies['Number of Countries'].max()}'\n
The Lowest Number of Countries Ever Any Movie Got is '{df_countries_least_movies['Title'][0]}' : '{df_countries_least_movies['Number of Countries'].min()}'\n
''')
Total '16' unique Number of Countries s were Given, They were Like this,
[27 20 19 18 15 11 10 9 8 7 6 5 4 3 2 1]
The Highest Number of Countries Ever Any Movie Got is 'Somewhere Else Tomorrow' : '27'
The Lowest Number of Countries Ever Any Movie Got is 'Citizen Architect: Samuel Mockbee and the Spirit of the Rural Studio' : '1'
netflix_countries_most_movies = df_countries_most_movies.loc[df_countries_most_movies['Netflix']==1].reset_index()
netflix_countries_most_movies = netflix_countries_most_movies.drop(['index'], axis = 1)
netflix_countries_least_movies = df_countries_least_movies.loc[df_countries_least_movies['Netflix']==1].reset_index()
netflix_countries_least_movies = netflix_countries_least_movies.drop(['index'], axis = 1)
netflix_countries_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Countries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 287 | A Shaun the Sheep Movie: Farmageddon | 2019 | 0 | 6.8 | 96 | Will Becher,Richard Phelan | Justin Fletcher,John Sparkes,Amalia Vitale,Kat... | Animation,Adventure,Comedy,Family,Fantasy,Sci-Fi | United Kingdom,France,Belgium,United States,Ch... | ... | There is something strange going on in the pea... | 86 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 10 |
| 1 | 2880 | The Thief and the Fool | 2013 | NR | 7.1 | NA | Richard Williams | Vincent Price,Bobbi Page,Steve Lively,Ed E. Ca... | Animation,Action,Adventure,Comedy,Family,Fanta... | United Kingdom,United States,Canada,Taiwan,Ire... | ... | When Tack upsets ZigZag the Vizier, the wizard... | 90 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 9 |
| 2 | 781 | Resident Evil: Afterlife | 2010 | 16 | 6.3 | 22 | Russell Mulcahy | Milla Jovovich,Oded Fehr,Ali Larter,Iain Glen,... | Action,Horror,Sci-Fi | France,Australia,Germany,United Kingdom,United... | ... | Years after the Raccoon City disaster, Alice i... | 94 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 8 |
| 3 | 554 | Resident Evil: Extinction | 2007 | 16 | 6.3 | 24 | Russell Mulcahy | Milla Jovovich,Oded Fehr,Ali Larter,Iain Glen,... | Action,Horror,Sci-Fi | France,Australia,Germany,United Kingdom,United... | ... | Years after the Raccoon City disaster, Alice i... | 94 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 8 |
| 4 | 1644 | Arctic Dogs | 2019 | 7 | 4.7 | 13 | Aaron Woodley | Jeremy Renner,Heidi Klum,James Franco,John Cle... | Animation,Adventure,Comedy,Family | India,United Kingdom,China,Canada,Japan,South ... | ... | Swifty the Arctic Fox (Jeremy Renner) works in... | 92 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 7 |
5 rows × 21 columns
fig = px.bar(y = netflix_countries_most_movies['Title'][:15],
x = netflix_countries_most_movies['Number of Countries'][:15],
color = netflix_countries_most_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Highest Number of Countries : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = netflix_countries_least_movies['Title'][:15],
x = netflix_countries_least_movies['Number of Countries'][:15],
color = netflix_countries_least_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Lowest Number of Countries : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
hulu_countries_most_movies = df_countries_most_movies.loc[df_countries_most_movies['Hulu']==1].reset_index()
hulu_countries_most_movies = hulu_countries_most_movies.drop(['index'], axis = 1)
hulu_countries_least_movies = df_countries_least_movies.loc[df_countries_least_movies['Hulu']==1].reset_index()
hulu_countries_least_movies = hulu_countries_least_movies.drop(['index'], axis = 1)
hulu_countries_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Countries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3552 | Monos | 2019 | 16 | 6.9 | 92 | Alejandro Landes | Sofia Buenaventura,Julián Giraldo,Karen Quinte... | Adventure,Drama,Thriller | Colombia,Argentina,Netherlands,Germany,Sweden,... | ... | Teenage commandos perform military training ex... | 102 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 10 |
| 1 | 3685 | Manderlay | 2005 | NR | 7.3 | 50 | Lars von Trier | Bryce Dallas Howard,Isaach De Bankolé,Danny Gl... | Drama | Denmark,Sweden,Netherlands,France,Germany,Unit... | ... | After gangster Mulligan's (Willem Dafoe's) car... | 139 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 8 |
| 2 | 16432 | Underground | 2016 | 18 | 8.1 | 96 | Emir Kusturica | Predrag 'Miki' Manojlovic,Lazar Ristovski,Mirj... | Comedy,Drama,Fantasy,War | Federal Republic of Yugoslavia,France,Germany,... | ... | NA | 170 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 8 |
| 3 | 16472 | Curious George | 2006 | 0 | 6.5 | NA | Matthew O'Callaghan | Frank Welker,Will Ferrell,Shane Baumel,Timyra-... | Animation,Adventure,Comedy,Family | United States,Germany,Taiwan,France,Canada,Sou... | ... | NA | 87 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 8 |
| 4 | 3947 | Sherlock Gnomes | 2018 | 7 | 5.2 | 27 | John Stevenson | Kelly Asbury,Mary J. Blige,Emily Blunt,Julio B... | Animation,Adventure,Comedy,Family,Fantasy,Myst... | United Kingdom,United States,India,Canada,Fran... | ... | Sherlock Gnomes (Johnny Depp) and his assistan... | 86 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 6 |
5 rows × 21 columns
fig = px.bar(y = hulu_countries_most_movies['Title'][:15],
x = hulu_countries_most_movies['Number of Countries'][:15],
color = hulu_countries_most_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Highest Number of Countries : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = hulu_countries_least_movies['Title'][:15],
x = hulu_countries_least_movies['Number of Countries'][:15],
color = hulu_countries_least_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Lowest Number of Countries : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
prime_video_countries_most_movies = df_countries_most_movies.loc[df_countries_most_movies['Prime Video']==1].reset_index()
prime_video_countries_most_movies = prime_video_countries_most_movies.drop(['index'], axis = 1)
prime_video_countries_least_movies = df_countries_least_movies.loc[df_countries_least_movies['Prime Video']==1].reset_index()
prime_video_countries_least_movies = prime_video_countries_least_movies.drop(['index'], axis = 1)
prime_video_countries_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Countries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6906 | Somewhere Else Tomorrow | 2013 | 13 | 7.5 | NA | Daniel Rintz | Issa Breibish,Kristian Bruun,Megan Gay,Grant J... | Documentary,Adventure,Drama,News | Germany,Canada,Vietnam,United Kingdom,Turkey,T... | ... | Promising young student Chi-Hao is sent away t... | 107 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 27 |
| 1 | 6333 | Poverty, Inc. | 2014 | NR | 7.7 | NA | Michael Matheson Miller | Robert Sirico | Documentary,History,News | United States,United Kingdom,Thailand,Swazilan... | ... | The lady is Mrs. Hilyard, a wealthy poetess wh... | 94 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 20 |
| 2 | 4382 | Samsara | 2011 | 13 | 8.5 | 76 | Ron Fricke | Balinese Tari Legong Dancers,Ni Made Megahadi ... | Documentary,Music | United States,Indonesia,Singapore,Thailand,Ken... | ... | NA | 102 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 19 |
| 3 | 8004 | Lost Kites | 2016 | NR | 7 | NA | Samuel Rich,Gabriella Fritz | NA | Documentary | United States,Belgium,Brazil,Cambodia,China,In... | ... | All fourteen-year-old Robbie Hendrick ever wan... | 50 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 18 |
| 4 | 10318 | Caffeinated | 2015 | NR | 6.6 | NA | Hanh Nguyen,Vishal Solanki | Jeremy Adams,Andrew Alcala,Sarah Allen,Grazian... | Documentary,Adventure,News | United States,Colombia,Ethiopia,Guatemala,Hond... | ... | NA | 80 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 15 |
5 rows × 21 columns
fig = px.bar(y = prime_video_countries_most_movies['Title'][:15],
x = prime_video_countries_most_movies['Number of Countries'][:15],
color = prime_video_countries_most_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Highest Number of Countries : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = prime_video_countries_least_movies['Title'][:15],
x = prime_video_countries_least_movies['Number of Countries'][:15],
color = prime_video_countries_least_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Lowest Number of Countries : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
disney_countries_most_movies = df_countries_most_movies.loc[df_countries_most_movies['Disney+']==1].reset_index()
disney_countries_most_movies = disney_countries_most_movies.drop(['index'], axis = 1)
disney_countries_least_movies = df_countries_least_movies.loc[df_countries_least_movies['Disney+']==1].reset_index()
disney_countries_least_movies = disney_countries_least_movies.drop(['index'], axis = 1)
disney_countries_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Countries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16012 | Mulan II | 2004 | 0 | 5.7 | 0 | Darrell Rooney,Lynne Southerland | Ming-Na Wen,BD Wong,Mark Moseley,Lucy Liu,Harv... | Animation,Action,Comedy,Family,Musical | United States,South Korea,Singapore,Russia,Mal... | ... | Tia and her brother Tony have supernatural pow... | 79 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 15 |
| 1 | 15984 | The Little Mermaid II: Return to the Sea | 2000 | 7 | 5.6 | 17 | Jim Kammerud,Brian Smith,Bill Speers | Jodi Benson,Samuel E. Wright,Tara Strong,Pat C... | Animation,Drama,Family,Fantasy,Musical | United States,Canada,Australia,Taiwan,Hong Kon... | ... | The Russo family and friends are headed to Tus... | 75 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 8 |
| 2 | 15960 | Thumbelina | 1994 | 0 | 6.4 | 27 | Don Bluth,Gary Goldman | Gino Conforti,Barbara Cook,Jodi Benson,Will Ry... | Animation,Family,Fantasy,Musical,Romance | Ireland,United States,Canada,United Kingdom,De... | ... | The sultan is grooming Aladdin as new vizier, ... | 86 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 8 |
| 3 | 16196 | The Flood | 2018 | 16 | 5.9 | 75 | Mikael Salomon | Morgan Freeman,Christian Slater,Randy Quaid,Mi... | Action,Crime,Drama,Thriller | United States,United Kingdom,Denmark,France,Ja... | ... | NA | 97 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 8 |
| 4 | 16069 | Into the Okavango | 2018 | 7 | 7.6 | NA | Neil Gelinas | Chris Boyes,Jack Boyes,Steve Boyes,Adjany Cost... | Documentary | United States,Angola,Botswana,Namibia,South Af... | ... | As Tarzan and Jane's one-year wedding annivers... | 88 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 5 |
5 rows × 21 columns
fig = px.bar(y = disney_countries_most_movies['Title'][:15],
x = disney_countries_most_movies['Number of Countries'][:15],
color = disney_countries_most_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Highest Number of Countries : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = disney_countries_least_movies['Title'][:15],
x = disney_countries_least_movies['Number of Countries'][:15],
color = disney_countries_least_movies['Number of Countries'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Countries'},
title = 'Movies with Lowest Number of Countries : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
The Movie with Highest Number of Countries Ever Got is '{df_countries_most_movies['Title'][0]}' : '{df_countries_most_movies['Number of Countries'].max()}'\n
The Movie with Lowest Number of Countries Ever Got is '{df_countries_least_movies['Title'][0]}' : '{df_countries_least_movies['Number of Countries'].min()}'\n
The Movie with Highest Number of Countries on 'Netflix' is '{netflix_countries_most_movies['Title'][0]}' : '{netflix_countries_most_movies['Number of Countries'].max()}'\n
The Movie with Lowest Number of Countries on 'Netflix' is '{netflix_countries_least_movies['Title'][0]}' : '{netflix_countries_least_movies['Number of Countries'].min()}'\n
The Movie with Highest Number of Countries on 'Hulu' is '{hulu_countries_most_movies['Title'][0]}' : '{hulu_countries_most_movies['Number of Countries'].max()}'\n
The Movie with Lowest Number of Countries on 'Hulu' is '{hulu_countries_least_movies['Title'][0]}' : '{hulu_countries_least_movies['Number of Countries'].min()}'\n
The Movie with Highest Number of Countries on 'Prime Video' is '{prime_video_countries_most_movies['Title'][0]}' : '{prime_video_countries_most_movies['Number of Countries'].max()}'\n
The Movie with Lowest Number of Countries on 'Prime Video' is '{prime_video_countries_least_movies['Title'][0]}' : '{prime_video_countries_least_movies['Number of Countries'].min()}'\n
The Movie with Highest Number of Countries on 'Disney+' is '{disney_countries_most_movies['Title'][0]}' : '{disney_countries_most_movies['Number of Countries'].max()}'\n
The Movie with Lowest Number of Countries on 'Disney+' is '{disney_countries_least_movies['Title'][0]}' : '{disney_countries_least_movies['Number of Countries'].min()}'\n
''')
The Movie with Highest Number of Countries Ever Got is 'Somewhere Else Tomorrow' : '27'
The Movie with Lowest Number of Countries Ever Got is 'Citizen Architect: Samuel Mockbee and the Spirit of the Rural Studio' : '1'
The Movie with Highest Number of Countries on 'Netflix' is 'A Shaun the Sheep Movie: Farmageddon' : '10'
The Movie with Lowest Number of Countries on 'Netflix' is 'My Sassy Girl' : '1'
The Movie with Highest Number of Countries on 'Hulu' is 'Monos' : '10'
The Movie with Lowest Number of Countries on 'Hulu' is 'Side Effects' : '1'
The Movie with Highest Number of Countries on 'Prime Video' is 'Somewhere Else Tomorrow' : '27'
The Movie with Lowest Number of Countries on 'Prime Video' is 'Citizen Architect: Samuel Mockbee and the Spirit of the Rural Studio' : '1'
The Movie with Highest Number of Countries on 'Disney+' is 'Mulan II' : '15'
The Movie with Lowest Number of Countries on 'Disney+' is 'The Swap' : '1'
print(f'''
Accross All Platforms the Average Number of Countries is '{round(df_movies_count_countries['Number of Countries'].mean(), ndigits = 2)}'\n
The Average Number of Countries on 'Netflix' is '{round(netflix_countries_movies['Number of Countries'].mean(), ndigits = 2)}'\n
The Average Number of Countries on 'Hulu' is '{round(hulu_countries_movies['Number of Countries'].mean(), ndigits = 2)}'\n
The Average Number of Countries on 'Prime Video' is '{round(prime_video_countries_movies['Number of Countries'].mean(), ndigits = 2)}'\n
The Average Number of Countries on 'Disney+' is '{round(disney_countries_movies['Number of Countries'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Number of Countries is '1.29'
The Average Number of Countries on 'Netflix' is '1.32'
The Average Number of Countries on 'Hulu' is '1.48'
The Average Number of Countries on 'Prime Video' is '1.27'
The Average Number of Countries on 'Disney+' is '1.37'
print(f'''
Accross All Platforms Total Count of Country is '{df_movies_count_countries['Number of Countries'].max()}'\n
Total Count of Country on 'Netflix' is '{netflix_countries_movies['Number of Countries'].max()}'\n
Total Count of Country on 'Hulu' is '{hulu_countries_movies['Number of Countries'].max()}'\n
Total Count of Country on 'Prime Video' is '{prime_video_countries_movies['Number of Countries'].max()}'\n
Total Count of Country on 'Disney+' is '{disney_countries_movies['Number of Countries'].max()}'\n
''')
Accross All Platforms Total Count of Country is '27'
Total Count of Country on 'Netflix' is '10'
Total Count of Country on 'Hulu' is '10'
Total Count of Country on 'Prime Video' is '27'
Total Count of Country on 'Disney+' is '15'
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_count_countries['Number of Countries'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_count_countries['Number of Countries'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Countries s Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_countries_movies['Number of Countries'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_countries_movies['Number of Countries'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_countries_movies['Number of Countries'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_countries_movies['Number of Countries'], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
df_lan = df_movies_country['Country'].str.split(',').apply(pd.Series).stack()
del df_movies_country['Country']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Country'
df_movies_country = df_movies_country.join(df_lan)
df_movies_country.drop_duplicates(inplace = True)
df_movies_country.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | United States |
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | United Kingdom |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | United States |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | United States |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | United States |
country_count = df_movies_country.groupby('Country')['Title'].count()
country_movies = df_movies_country.groupby('Country')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
country_data_movies = pd.concat([country_count, country_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
country_data_movies = country_data_movies.sort_values(by = 'Movies Count', ascending = False)
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_country_movies = country_data_movies[country_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_country_movies = netflix_country_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_country_movies = country_data_movies[country_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_country_movies = hulu_country_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
prime_video_country_movies = country_data_movies[country_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_country_movies = prime_video_country_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
disney_country_movies = country_data_movies[country_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_country_movies = disney_country_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
# Country with Movies Counts - All Platforms Combined
country_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]
| Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 159 | United States | 10771 | 1800 | 790 | 8040 | 546 |
| 158 | United Kingdom | 1815 | 354 | 147 | 1331 | 61 |
| 26 | Canada | 1175 | 224 | 93 | 857 | 40 |
| 65 | India | 1143 | 569 | 9 | 659 | 4 |
| 52 | France | 791 | 188 | 83 | 537 | 16 |
| 54 | Germany | 527 | 125 | 44 | 380 | 5 |
| 72 | Italy | 450 | 58 | 18 | 386 | 1 |
| 8 | Australia | 342 | 65 | 24 | 244 | 21 |
| 138 | Spain | 333 | 125 | 24 | 195 | 1 |
| 74 | Japan | 305 | 95 | 45 | 168 | 7 |
fig = px.bar(x = country_data_movies['Country'][:50],
y = country_data_movies['Movies Count'][:50],
color = country_data_movies['Movies Count'][:50],
color_continuous_scale = 'Teal_r',
labels = { 'x' : 'Country', 'y' : 'Movies Count'},
title = 'Major Countries : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.choropleth(data_frame = country_data_movies, locations = 'Country', locationmode = 'country names', color = 'Movies Count', color_continuous_scale = 'deep')
fig.show()
df_country_high_movies = country_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_country_high_movies = df_country_high_movies.drop(['index'], axis = 1)
# filter = (country_data_movies['Movies Count'] == (country_data_movies['Movies Count'].max()))
# df_country_high_movies = country_data_movies[filter]
# highest_rated_movies = country_data_movies.loc[country_data_movies['Movies Count'].idxmax()]
print('\nCountry with Highest Ever Movies Count are : All Platforms Combined\n')
df_country_high_movies.head(5)
Country with Highest Ever Movies Count are : All Platforms Combined
| Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United States | 10771 | 1800 | 790 | 8040 | 546 |
| 1 | United Kingdom | 1815 | 354 | 147 | 1331 | 61 |
| 2 | Canada | 1175 | 224 | 93 | 857 | 40 |
| 3 | India | 1143 | 569 | 9 | 659 | 4 |
| 4 | France | 791 | 188 | 83 | 537 | 16 |
fig = px.bar(y = df_country_high_movies['Country'][:15],
x = df_country_high_movies['Movies Count'][:15],
color = df_country_high_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Country', 'x' : 'Movies Count'},
title = 'Country with Highest Movies : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_country_low_movies = country_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_country_low_movies = df_country_low_movies.drop(['index'], axis = 1)
# filter = (country_data_movies['Movies Count'] == (country_data_movies['Movies Count'].min()))
# df_country_low_movies = country_data_movies[filter]
print('\nCountry with Lowest Ever Movies Count are : All Platforms Combined\n')
df_country_low_movies.head(5)
Country with Lowest Ever Movies Count are : All Platforms Combined
| Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Macao | 1 | 0 | 0 | 1 | 0 |
| 1 | U.S. Virgin Islands | 1 | 0 | 0 | 1 | 0 |
| 2 | Botswana | 1 | 0 | 0 | 0 | 1 |
| 3 | Vanuatu | 1 | 0 | 0 | 1 | 0 |
| 4 | Bahrain | 1 | 0 | 0 | 1 | 0 |
fig = px.bar(y = df_country_low_movies['Country'][:15],
x = df_country_low_movies['Movies Count'][:15],
color = df_country_low_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Country', 'x' : 'Movies Count'},
title = 'Country with Lowest Movies Count : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{country_data_movies['Country'].unique().shape[0]}' unique Country Count s were Given, They were Like this,\n
{country_data_movies.sort_values(by = 'Movies Count', ascending = False)['Country'].unique()[:5]}\n
The Highest Ever Movies Count Ever Any Movie Got is '{df_country_high_movies['Country'][0]}' : '{df_country_high_movies['Movies Count'].max()}'\n
The Lowest Ever Movies Count Ever Any Movie Got is '{df_country_low_movies['Country'][0]}' : '{df_country_low_movies['Movies Count'].min()}'\n
''')
Total '169' unique Country Count s were Given, They were Like this,
['United States' 'United Kingdom' 'Canada' 'India' 'France']
The Highest Ever Movies Count Ever Any Movie Got is 'United States' : '10771'
The Lowest Ever Movies Count Ever Any Movie Got is 'Macao' : '1'
fig = px.pie(country_data_movies[:10], names = 'Country', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Country')
fig.show()
# netflix_country_movies = country_data_movies[country_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_country_movies = netflix_country_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_country_high_movies = df_country_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_country_high_movies = netflix_country_high_movies.drop(['index'], axis = 1)
netflix_country_low_movies = df_country_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_country_low_movies = netflix_country_low_movies.drop(['index'], axis = 1)
netflix_country_high_movies.head(5)
| Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United States | 10771 | 1800 | 790 | 8040 | 546 |
| 1 | India | 1143 | 569 | 9 | 659 | 4 |
| 2 | United Kingdom | 1815 | 354 | 147 | 1331 | 61 |
| 3 | Canada | 1175 | 224 | 93 | 857 | 40 |
| 4 | France | 791 | 188 | 83 | 537 | 16 |
fig = px.bar(x = netflix_country_high_movies['Country'][:15],
y = netflix_country_high_movies['Netflix'][:15],
color = netflix_country_high_movies['Netflix'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Country', 'x' : 'Movies Count'},
title = 'Country with Highest Movies : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.choropleth(data_frame = netflix_country_movies, locations = 'Country', locationmode = 'country names', color = 'Netflix', color_continuous_scale = 'Reds')
fig.show()
# hulu_country_movies = country_data_movies[country_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_country_movies = hulu_country_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_country_high_movies = df_country_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_country_high_movies = hulu_country_high_movies.drop(['index'], axis = 1)
hulu_country_low_movies = df_country_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_country_low_movies = hulu_country_low_movies.drop(['index'], axis = 1)
hulu_country_high_movies.head(5)
| Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United States | 10771 | 1800 | 790 | 8040 | 546 |
| 1 | United Kingdom | 1815 | 354 | 147 | 1331 | 61 |
| 2 | Canada | 1175 | 224 | 93 | 857 | 40 |
| 3 | France | 791 | 188 | 83 | 537 | 16 |
| 4 | Japan | 305 | 95 | 45 | 168 | 7 |
fig = px.bar(x = hulu_country_high_movies['Country'][:15],
y = hulu_country_high_movies['Hulu'][:15],
color = hulu_country_high_movies['Hulu'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Country', 'x' : 'Movies Count'},
title = 'Country with Highest Movies : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.choropleth(data_frame = hulu_country_movies, locations = 'Country', locationmode = 'country names', color = 'Hulu', color_continuous_scale = 'Greens')
fig.show()
# prime_video_country_movies = country_data_movies[country_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_country_movies = prime_video_country_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_country_high_movies = df_country_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_country_high_movies = prime_video_country_high_movies.drop(['index'], axis = 1)
prime_video_country_low_movies = df_country_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_country_low_movies = prime_video_country_low_movies.drop(['index'], axis = 1)
prime_video_country_high_movies.head(5)
| Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United States | 10771 | 1800 | 790 | 8040 | 546 |
| 1 | United Kingdom | 1815 | 354 | 147 | 1331 | 61 |
| 2 | Canada | 1175 | 224 | 93 | 857 | 40 |
| 3 | India | 1143 | 569 | 9 | 659 | 4 |
| 4 | France | 791 | 188 | 83 | 537 | 16 |
fig = px.bar(x = prime_video_country_high_movies['Country'][:15],
y = prime_video_country_high_movies['Prime Video'][:15],
color = prime_video_country_high_movies['Prime Video'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Country', 'x' : 'Movies Count'},
title = 'Country with Highest Movies : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.choropleth(data_frame = prime_video_country_movies, locations = 'Country', locationmode = 'country names', color = 'Prime Video', color_continuous_scale = 'Blues')
fig.show()
# disney_country_movies = country_data_movies[country_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_country_movies = disney_country_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_country_high_movies = df_country_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_country_high_movies = disney_country_high_movies.drop(['index'], axis = 1)
disney_country_low_movies = df_country_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_country_low_movies = disney_country_low_movies.drop(['index'], axis = 1)
disney_country_high_movies.head(5)
| Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United States | 10771 | 1800 | 790 | 8040 | 546 |
| 1 | United Kingdom | 1815 | 354 | 147 | 1331 | 61 |
| 2 | Canada | 1175 | 224 | 93 | 857 | 40 |
| 3 | Australia | 342 | 65 | 24 | 244 | 21 |
| 4 | France | 791 | 188 | 83 | 537 | 16 |
fig = px.bar(x = disney_country_high_movies['Country'][:15],
y = disney_country_high_movies['Disney+'][:15],
color = disney_country_high_movies['Disney+'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Country', 'x' : 'Movies Count'},
title = 'Country with Highest Movies : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.choropleth(data_frame = disney_country_movies, locations = 'Country', locationmode = 'country names', color = 'Disney+', color_continuous_scale = 'BuPu')
fig.show()
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(country_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(country_data_movies['Movies Count'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Country Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(disney_country_movies['Disney+'][:50], color = 'darkblue', legend = True, kde = True)
sns.histplot(prime_video_country_movies['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_country_movies['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_country_movies['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()
print(f'''
The Country with Highest Movies Count Ever Got is '{df_country_high_movies['Country'][0]}' : '{df_country_high_movies['Movies Count'].max()}'\n
The Country with Lowest Movies Count Ever Got is '{df_country_low_movies['Country'][0]}' : '{df_country_low_movies['Movies Count'].min()}'\n
The Country with Highest Movies Count on 'Netflix' is '{netflix_country_high_movies['Country'][0]}' : '{netflix_country_high_movies['Netflix'].max()}'\n
The Country with Lowest Movies Count on 'Netflix' is '{netflix_country_low_movies['Country'][0]}' : '{netflix_country_low_movies['Netflix'].min()}'\n
The Country with Highest Movies Count on 'Hulu' is '{hulu_country_high_movies['Country'][0]}' : '{hulu_country_high_movies['Hulu'].max()}'\n
The Country with Lowest Movies Count on 'Hulu' is '{hulu_country_low_movies['Country'][0]}' : '{hulu_country_low_movies['Hulu'].min()}'\n
The Country with Highest Movies Count on 'Prime Video' is '{prime_video_country_high_movies['Country'][0]}' : '{prime_video_country_high_movies['Prime Video'].max()}'\n
The Country with Lowest Movies Count on 'Prime Video' is '{prime_video_country_low_movies['Country'][0]}' : '{prime_video_country_low_movies['Prime Video'].min()}'\n
The Country with Highest Movies Count on 'Disney+' is '{disney_country_high_movies['Country'][0]}' : '{disney_country_high_movies['Disney+'].max()}'\n
The Country with Lowest Movies Count on 'Disney+' is '{disney_country_low_movies['Country'][0]}' : '{disney_country_low_movies['Disney+'].min()}'\n
''')
The Country with Highest Movies Count Ever Got is 'United States' : '10771'
The Country with Lowest Movies Count Ever Got is 'Macao' : '1'
The Country with Highest Movies Count on 'Netflix' is 'United States' : '1800'
The Country with Lowest Movies Count on 'Netflix' is 'Macao' : '0'
The Country with Highest Movies Count on 'Hulu' is 'United States' : '790'
The Country with Lowest Movies Count on 'Hulu' is 'Macao' : '0'
The Country with Highest Movies Count on 'Prime Video' is 'United States' : '8040'
The Country with Lowest Movies Count on 'Prime Video' is 'Federal Republic of Yugoslavia' : '0'
The Country with Highest Movies Count on 'Disney+' is 'United States' : '546'
The Country with Lowest Movies Count on 'Disney+' is 'Albania' : '0'
# Distribution of movies country in each platform
plt.figure(figsize = (20, 5))
plt.title('Country with Movies Count for All Platforms')
sns.violinplot(x = country_data_movies['Movies Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()
# Distribution of Country Movies Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_country_movies['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_country_movies['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_country_movies['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_country_movies['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()
print(f'''
Accross All Platforms the Average Movies Count of Country is '{round(country_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
The Average Movies Count of Country on 'Netflix' is '{round(netflix_country_movies['Netflix'].mean(), ndigits = 2)}'\n
The Average Movies Count of Country on 'Hulu' is '{round(hulu_country_movies['Hulu'].mean(), ndigits = 2)}'\n
The Average Movies Count of Country on 'Prime Video' is '{round(prime_video_country_movies['Prime Video'].mean(), ndigits = 2)}'\n
The Average Movies Count of Country on 'Disney+' is '{round(disney_country_movies['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Movies Count of Country is '127.17'
The Average Movies Count of Country on 'Netflix' is '45.85'
The Average Movies Count of Country on 'Hulu' is '23.56'
The Average Movies Count of Country on 'Prime Video' is '94.19'
The Average Movies Count of Country on 'Disney+' is '17.41'
print(f'''
Accross All Platforms Total Count of Country is '{country_data_movies['Country'].unique().shape[0]}'\n
Total Count of Country on 'Netflix' is '{netflix_country_movies['Country'].unique().shape[0]}'\n
Total Count of Country on 'Hulu' is '{hulu_country_movies['Country'].unique().shape[0]}'\n
Total Count of Country on 'Prime Video' is '{prime_video_country_movies['Country'].unique().shape[0]}'\n
Total Count of Country on 'Disney+' is '{disney_country_movies['Country'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Country is '169'
Total Count of Country on 'Netflix' is '102'
Total Count of Country on 'Hulu' is '66'
Total Count of Country on 'Prime Video' is '163'
Total Count of Country on 'Disney+' is '44'
plt.figure(figsize = (20, 5))
sns.lineplot(x = country_data_movies['Country'][:10], y = country_data_movies['Netflix'][:10], color = 'red')
sns.lineplot(x = country_data_movies['Country'][:10], y = country_data_movies['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = country_data_movies['Country'][:10], y = country_data_movies['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = country_data_movies['Country'][:10], y = country_data_movies['Disney+'][:10], color = 'darkblue')
plt.xlabel('Country', fontsize = 20)
plt.ylabel('Movies Count', fontsize = 20)
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
n_co_ax1 = sns.lineplot(y = country_data_movies['Country'][:10], x = country_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_co_ax2 = sns.lineplot(y = country_data_movies['Country'][:10], x = country_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_co_ax3 = sns.lineplot(y = country_data_movies['Country'][:10], x = country_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_co_ax4 = sns.lineplot(y = country_data_movies['Country'][:10], x = country_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_co_ax1.title.set_text(labels[0])
h_co_ax2.title.set_text(labels[1])
p_co_ax3.title.set_text(labels[2])
d_co_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_co_ax1 = sns.barplot(y = netflix_country_movies['Country'][:10], x = netflix_country_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_co_ax2 = sns.barplot(y = hulu_country_movies['Country'][:10], x = hulu_country_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_co_ax3 = sns.barplot(y = prime_video_country_movies['Country'][:10], x = prime_video_country_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_co_ax4 = sns.barplot(y = disney_country_movies['Country'][:10], x = disney_country_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_co_ax1.title.set_text(labels[0])
h_co_ax2.title.set_text(labels[1])
p_co_ax3.title.set_text(labels[2])
d_co_ax4.title.set_text(labels[3])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Country Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_country_movies['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_country_movies['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_country_movies['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_country_movies['Disney+'][:10], color = 'darkblue', legend = True)
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_co_ax1 = sns.barplot(y = country_data_movies['Country'][:10], x = country_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_co_ax2 = sns.barplot(y = country_data_movies['Country'][:10], x = country_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_co_ax3 = sns.barplot(y = country_data_movies['Country'][:10], x = country_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_co_ax4 = sns.barplot(y = country_data_movies['Country'][:10], x = country_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_co_ax1.title.set_text(labels[0])
h_co_ax2.title.set_text(labels[1])
p_co_ax3.title.set_text(labels[2])
d_co_ax4.title.set_text(labels[3])
plt.show()
df_movies_mixed_countries.drop(df_movies_mixed_countries.loc[df_movies_mixed_countries['Country'] == "NA"].index, inplace = True)
# df_movies_mixed_countries = df_movies_mixed_countries[df_movies_mixed_countries.Country != "NA"]
df_movies_mixed_countries.drop(df_movies_mixed_countries.loc[df_movies_mixed_countries['Number of Countries'] == 1].index, inplace = True)
df_movies_mixed_countries.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Countries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | ... | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 2 |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | ... | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 4 |
| 6 | 7 | The Pianist | 2002 | 16 | 8.5 | 95 | Roman Polanski | Adrien Brody,Emilia Fox,Michal Zebrowski,Ed St... | Biography,Drama,Music,War | United Kingdom,France,Poland,Germany,United St... | ... | In this adaptation of the autobiography "The P... | 150 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 5 |
| 9 | 10 | Inglourious Basterds | 2009 | 16 | 8.3 | 89 | Quentin Tarantino | Brad Pitt,Mélanie Laurent,Christoph Waltz,Eli ... | Adventure,Drama,War | United States,Germany | ... | In German-occupied France, young Jewish refuge... | 153 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 2 |
| 12 | 13 | Pan's Labyrinth | 2006 | 16 | 8.2 | 95 | Guillermo del Toro | Ivana Baquero,Sergi López,Maribel Verdú,Doug J... | Drama,Fantasy,War | Mexico,Spain | ... | In 1944 Falangist Spain, a girl, fascinated wi... | 118 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 2 |
5 rows × 21 columns
mixed_countries_count = df_movies_mixed_countries.groupby('Country')['Title'].count()
mixed_countries_movies = df_movies_mixed_countries.groupby('Country')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_countries_data_movies = pd.concat([mixed_countries_count, mixed_countries_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count', 'Country' : 'Mixed Country'})
mixed_countries_data_movies = mixed_countries_data_movies.sort_values(by = 'Movies Count', ascending = False)
mixed_countries_data_movies.head(5)
| Mixed Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 970 | United Kingdom,United States | 182 | 43 | 18 | 120 | 13 |
| 1032 | United States,Canada | 160 | 36 | 21 | 102 | 14 |
| 162 | Canada,United States | 150 | 25 | 15 | 104 | 11 |
| 1225 | United States,United Kingdom | 104 | 19 | 17 | 61 | 16 |
| 1161 | United States,Mexico | 48 | 11 | 6 | 28 | 3 |
# Mixed Country with Movies Counts - All Platforms Combined
mixed_countries_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]
| Mixed Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 970 | United Kingdom,United States | 182 | 43 | 18 | 120 | 13 |
| 1032 | United States,Canada | 160 | 36 | 21 | 102 | 14 |
| 162 | Canada,United States | 150 | 25 | 15 | 104 | 11 |
| 1225 | United States,United Kingdom | 104 | 19 | 17 | 61 | 16 |
| 1161 | United States,Mexico | 48 | 11 | 6 | 28 | 3 |
| 276 | France,Belgium | 41 | 19 | 3 | 21 | 0 |
| 530 | Italy,France | 41 | 2 | 5 | 36 | 0 |
| 1089 | United States,Germany | 39 | 8 | 5 | 28 | 1 |
| 423 | Germany,United States | 33 | 10 | 2 | 23 | 1 |
| 1077 | United States,France | 30 | 6 | 3 | 18 | 6 |
df_mixed_countries_high_movies = mixed_countries_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_mixed_countries_high_movies = df_mixed_countries_high_movies.drop(['index'], axis = 1)
# filter = (mixed_countries_data_movies['Movies Count'] = = (mixed_countries_data_movies['Movies Count'].max()))
# df_mixed_countries_high_movies = mixed_countries_data_movies[filter]
# highest_rated_movies = mixed_countries_data_movies.loc[mixed_countries_data_movies['Movies Count'].idxmax()]
print('\nMixed Country with Highest Ever Movies Count are : All Platforms Combined\n')
df_mixed_countries_high_movies.head(5)
Mixed Country with Highest Ever Movies Count are : All Platforms Combined
| Mixed Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United Kingdom,United States | 182 | 43 | 18 | 120 | 13 |
| 1 | United States,Canada | 160 | 36 | 21 | 102 | 14 |
| 2 | Canada,United States | 150 | 25 | 15 | 104 | 11 |
| 3 | United States,United Kingdom | 104 | 19 | 17 | 61 | 16 |
| 4 | United States,Mexico | 48 | 11 | 6 | 28 | 3 |
fig = px.bar(y = df_mixed_countries_high_movies['Mixed Country'][:15],
x = df_mixed_countries_high_movies['Movies Count'][:15],
color = df_mixed_countries_high_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Country'},
title = 'Movies with Highest Number of Mixed Countries : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_mixed_countries_low_movies = mixed_countries_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_mixed_countries_low_movies = df_mixed_countries_low_movies.drop(['index'], axis = 1)
# filter = (mixed_countries_data_movies['Movies Count'] = = (mixed_countries_data_movies['Movies Count'].min()))
# df_mixed_countries_low_movies = mixed_countries_data_movies[filter]
print('\nMixed Country with Lowest Ever Movies Count are : All Platforms Combined\n')
df_mixed_countries_low_movies.head(5)
Mixed Country with Lowest Ever Movies Count are : All Platforms Combined
| Mixed Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United Kingdom,Sweden,Cayman Islands | 1 | 0 | 0 | 1 | 0 |
| 1 | Canada,Yugoslavia | 1 | 0 | 0 | 1 | 0 |
| 2 | Chile,Argentina,Belgium | 1 | 0 | 1 | 0 | 0 |
| 3 | Chile,Argentina,France,Spain,United States | 1 | 1 | 0 | 0 | 0 |
| 4 | France,Estonia,Germany | 1 | 0 | 0 | 1 | 0 |
fig = px.bar(y = df_mixed_countries_low_movies['Mixed Country'][:15],
x = df_mixed_countries_low_movies['Movies Count'][:15],
color = df_mixed_countries_low_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Country'},
title = 'Movies with Lowest Number of Mixed Countries : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_movies_countries['Country'].count()}' Titles are available on All Platforms, out of which\n
You Can Choose to see Movies from Total '{mixed_countries_data_movies['Mixed Country'].unique().shape[0]}' Mixed Country, They were Like this, \n
{mixed_countries_data_movies.sort_values(by = 'Movies Count', ascending = False)['Mixed Country'].head(5).unique()} etc. \n
The Mixed Country with Highest Movies Count have '{mixed_countries_data_movies['Movies Count'].max()}' Movies Available is '{df_mixed_countries_high_movies['Mixed Country'][0]}', &\n
The Mixed Country with Lowest Movies Count have '{mixed_countries_data_movies['Movies Count'].min()}' Movies Available is '{df_mixed_countries_low_movies['Mixed Country'][0]}'
''')
Total '16620' Titles are available on All Platforms, out of which
You Can Choose to see Movies from Total '1279' Mixed Country, They were Like this,
['United Kingdom,United States' 'United States,Canada'
'Canada,United States' 'United States,United Kingdom'
'United States,Mexico'] etc.
The Mixed Country with Highest Movies Count have '182' Movies Available is 'United Kingdom,United States', &
The Mixed Country with Lowest Movies Count have '1' Movies Available is 'United Kingdom,Sweden,Cayman Islands'
fig = px.pie(mixed_countries_data_movies[:10], names = 'Mixed Country', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Mixed Country')
fig.show()
# netflix_mixed_countries_movies = mixed_countries_data_movies[mixed_countries_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_countries_movies = netflix_mixed_countries_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_mixed_countries_high_movies = df_mixed_countries_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_countries_high_movies = netflix_mixed_countries_high_movies.drop(['index'], axis = 1)
netflix_mixed_countries_low_movies = df_mixed_countries_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_countries_low_movies = netflix_mixed_countries_low_movies.drop(['index'], axis = 1)
netflix_mixed_countries_high_movies.head(5)
| Mixed Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United Kingdom,United States | 182 | 43 | 18 | 120 | 13 |
| 1 | United States,Canada | 160 | 36 | 21 | 102 | 14 |
| 2 | Canada,United States | 150 | 25 | 15 | 104 | 11 |
| 3 | United States,United Kingdom | 104 | 19 | 17 | 61 | 16 |
| 4 | France,Belgium | 41 | 19 | 3 | 21 | 0 |
# hulu_mixed_countries_movies = mixed_countries_data_movies[mixed_countries_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_countries_movies = hulu_mixed_countries_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_mixed_countries_high_movies = df_mixed_countries_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_countries_high_movies = hulu_mixed_countries_high_movies.drop(['index'], axis = 1)
hulu_mixed_countries_low_movies = df_mixed_countries_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_countries_low_movies = hulu_mixed_countries_low_movies.drop(['index'], axis = 1)
hulu_mixed_countries_high_movies.head(5)
| Mixed Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United States,Canada | 160 | 36 | 21 | 102 | 14 |
| 1 | United Kingdom,United States | 182 | 43 | 18 | 120 | 13 |
| 2 | United States,United Kingdom | 104 | 19 | 17 | 61 | 16 |
| 3 | Canada,United States | 150 | 25 | 15 | 104 | 11 |
| 4 | United States,Mexico | 48 | 11 | 6 | 28 | 3 |
# prime_video_mixed_countries_movies = mixed_countries_data_movies[mixed_countries_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_countries_movies = prime_video_mixed_countries_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_mixed_countries_high_movies = df_mixed_countries_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_countries_high_movies = prime_video_mixed_countries_high_movies.drop(['index'], axis = 1)
prime_video_mixed_countries_low_movies = df_mixed_countries_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_countries_low_movies = prime_video_mixed_countries_low_movies.drop(['index'], axis = 1)
prime_video_mixed_countries_high_movies.head(5)
| Mixed Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United Kingdom,United States | 182 | 43 | 18 | 120 | 13 |
| 1 | Canada,United States | 150 | 25 | 15 | 104 | 11 |
| 2 | United States,Canada | 160 | 36 | 21 | 102 | 14 |
| 3 | United States,United Kingdom | 104 | 19 | 17 | 61 | 16 |
| 4 | Italy,France | 41 | 2 | 5 | 36 | 0 |
# disney_mixed_countries_movies = mixed_countries_data_movies[mixed_countries_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_countries_movies = disney_mixed_countries_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_mixed_countries_high_movies = df_mixed_countries_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_countries_high_movies = disney_mixed_countries_high_movies.drop(['index'], axis = 1)
disney_mixed_countries_low_movies = df_mixed_countries_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_countries_low_movies = disney_mixed_countries_low_movies.drop(['index'], axis = 1)
disney_mixed_countries_high_movies.head(5)
| Mixed Country | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | United States,United Kingdom | 104 | 19 | 17 | 61 | 16 |
| 1 | United States,Canada | 160 | 36 | 21 | 102 | 14 |
| 2 | United Kingdom,United States | 182 | 43 | 18 | 120 | 13 |
| 3 | Canada,United States | 150 | 25 | 15 | 104 | 11 |
| 4 | United States,Australia | 24 | 7 | 2 | 9 | 8 |
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_countries_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_countries_data_movies['Movies Count'], ax = ax[1])
plt.show()
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_mixed_countries_movies = mixed_countries_data_movies[mixed_countries_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_countries_movies = netflix_mixed_countries_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_mixed_countries_movies = mixed_countries_data_movies[mixed_countries_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_countries_movies = hulu_mixed_countries_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
prime_video_mixed_countries_movies = mixed_countries_data_movies[mixed_countries_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_countries_movies = prime_video_mixed_countries_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
disney_mixed_countries_movies = mixed_countries_data_movies[mixed_countries_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_countries_movies = disney_mixed_countries_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Country Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_mixed_countries_movies['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_countries_movies['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_countries_movies['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_countries_movies['Disney+'][:100], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
print(f'''
The Mixed Country with Highest Movies Count Ever Got is '{df_mixed_countries_high_movies['Mixed Country'][0]}' : '{df_mixed_countries_high_movies['Movies Count'].max()}'\n
The Mixed Country with Lowest Movies Count Ever Got is '{df_mixed_countries_low_movies['Mixed Country'][0]}' : '{df_mixed_countries_low_movies['Movies Count'].min()}'\n
The Mixed Country with Highest Movies Count on 'Netflix' is '{netflix_mixed_countries_high_movies['Mixed Country'][0]}' : '{netflix_mixed_countries_high_movies['Netflix'].max()}'\n
The Mixed Country with Lowest Movies Count on 'Netflix' is '{netflix_mixed_countries_low_movies['Mixed Country'][0]}' : '{netflix_mixed_countries_low_movies['Netflix'].min()}'\n
The Mixed Country with Highest Movies Count on 'Hulu' is '{hulu_mixed_countries_high_movies['Mixed Country'][0]}' : '{hulu_mixed_countries_high_movies['Hulu'].max()}'\n
The Mixed Country with Lowest Movies Count on 'Hulu' is '{hulu_mixed_countries_low_movies['Mixed Country'][0]}' : '{hulu_mixed_countries_low_movies['Hulu'].min()}'\n
The Mixed Country with Highest Movies Count on 'Prime Video' is '{prime_video_mixed_countries_high_movies['Mixed Country'][0]}' : '{prime_video_mixed_countries_high_movies['Prime Video'].max()}'\n
The Mixed Country with Lowest Movies Count on 'Prime Video' is '{prime_video_mixed_countries_low_movies['Mixed Country'][0]}' : '{prime_video_mixed_countries_low_movies['Prime Video'].min()}'\n
The Mixed Country with Highest Movies Count on 'Disney+' is '{disney_mixed_countries_high_movies['Mixed Country'][0]}' : '{disney_mixed_countries_high_movies['Disney+'].max()}'\n
The Mixed Country with Lowest Movies Count on 'Disney+' is '{disney_mixed_countries_low_movies['Mixed Country'][0]}' : '{disney_mixed_countries_low_movies['Disney+'].min()}'\n
''')
The Mixed Country with Highest Movies Count Ever Got is 'United Kingdom,United States' : '182'
The Mixed Country with Lowest Movies Count Ever Got is 'United Kingdom,Sweden,Cayman Islands' : '1'
The Mixed Country with Highest Movies Count on 'Netflix' is 'United Kingdom,United States' : '43'
The Mixed Country with Lowest Movies Count on 'Netflix' is 'Israel,Switzerland,Germany,France' : '0'
The Mixed Country with Highest Movies Count on 'Hulu' is 'United States,Canada' : '21'
The Mixed Country with Lowest Movies Count on 'Hulu' is 'Israel,Switzerland,Germany,France' : '0'
The Mixed Country with Highest Movies Count on 'Prime Video' is 'United Kingdom,United States' : '120'
The Mixed Country with Lowest Movies Count on 'Prime Video' is 'Lebanon,Qatar' : '0'
The Mixed Country with Highest Movies Count on 'Disney+' is 'United States,United Kingdom' : '16'
The Mixed Country with Lowest Movies Count on 'Disney+' is 'Israel,Switzerland,Germany,France' : '0'
print(f'''
Accross All Platforms the Average Movies Count of Mixed Country is '{round(mixed_countries_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Country on 'Netflix' is '{round(netflix_mixed_countries_movies['Netflix'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Country on 'Hulu' is '{round(hulu_mixed_countries_movies['Hulu'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Country on 'Prime Video' is '{round(prime_video_mixed_countries_movies['Prime Video'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Country on 'Disney+' is '{round(disney_mixed_countries_movies['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Movies Count of Mixed Country is '2.4'
The Average Movies Count of Mixed Country on 'Netflix' is '1.77'
The Average Movies Count of Mixed Country on 'Hulu' is '1.67'
The Average Movies Count of Mixed Country on 'Prime Video' is '2.21'
The Average Movies Count of Mixed Country on 'Disney+' is '2.46'
print(f'''
Accross All Platforms Total Count of Mixed Country is '{mixed_countries_data_movies['Mixed Country'].unique().shape[0]}'\n
Total Count of Mixed Country on 'Netflix' is '{netflix_mixed_countries_movies['Mixed Country'].unique().shape[0]}'\n
Total Count of Mixed Country on 'Hulu' is '{hulu_mixed_countries_movies['Mixed Country'].unique().shape[0]}'\n
Total Count of Mixed Country on 'Prime Video' is '{prime_video_mixed_countries_movies['Mixed Country'].unique().shape[0]}'\n
Total Count of Mixed Country on 'Disney+' is '{disney_mixed_countries_movies['Mixed Country'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Mixed Country is '1279'
Total Count of Mixed Country on 'Netflix' is '399'
Total Count of Mixed Country on 'Hulu' is '175'
Total Count of Mixed Country on 'Prime Video' is '943'
Total Count of Mixed Country on 'Disney+' is '52'
plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_countries_data_movies['Mixed Country'][:5], y = mixed_countries_data_movies['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_countries_data_movies['Mixed Country'][:5], y = mixed_countries_data_movies['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_countries_data_movies['Mixed Country'][:5], y = mixed_countries_data_movies['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_countries_data_movies['Mixed Country'][:5], y = mixed_countries_data_movies['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Country', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_co_ax1 = sns.barplot(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_co_ax2 = sns.barplot(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_co_ax3 = sns.barplot(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_co_ax4 = sns.barplot(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_co_ax1.title.set_text(labels[0])
h_co_ax2.title.set_text(labels[1])
p_co_ax3.title.set_text(labels[2])
d_co_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
n_mco_ax1 = sns.lineplot(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_mco_ax2 = sns.lineplot(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_mco_ax3 = sns.lineplot(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_mco_ax4 = sns.lineplot(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_mco_ax1.title.set_text(labels[0])
h_mco_ax2.title.set_text(labels[1])
p_mco_ax3.title.set_text(labels[2])
d_mco_ax4.title.set_text(labels[3])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Country Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_countries_movies['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_countries_movies['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_countries_movies['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_countries_movies['Disney+'][:50], color = 'darkblue', legend = True)
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_mco_ax1 = sns.barplot(y = netflix_mixed_countries_movies['Mixed Country'][:10], x = netflix_mixed_countries_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_mco_ax2 = sns.barplot(y = hulu_mixed_countries_movies['Mixed Country'][:10], x = hulu_mixed_countries_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_mco_ax3 = sns.barplot(y = prime_video_mixed_countries_movies['Mixed Country'][:10], x = prime_video_mixed_countries_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_mco_ax4 = sns.barplot(y = disney_mixed_countries_movies['Mixed Country'][:10], x = disney_mixed_countries_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_mco_ax1.title.set_text(labels[0])
h_mco_ax2.title.set_text(labels[1])
p_mco_ax3.title.set_text(labels[2])
d_mco_ax4.title.set_text(labels[3])
plt.show()
fig = go.Figure(go.Funnel(y = mixed_countries_data_movies['Mixed Country'][:10], x = mixed_countries_data_movies['Movies Count'][:10]))
fig.show()